<strong>ATP Metabolism in RBC as Potential Biomarker for Post-Exercise Hypotension and a Therapeutic Target for Cardiovascular Drugs</strong>
Bibliographic record
Abstract
The importance of adenosine and ATP in regulating many biological functions has long been recognized, especially for their effects on the cardiovascular homeostasis which may be used for management of hypertension and cardiovascular diseases. In response to ischemia and cardiovascular injury, ATP is broken down to release adenosine. The activity of adenosine is very short lived because it is rapidly taken up by myocardial and endothelial cells, erythrocytes (RBC), and also rapidly metabolized to oxypurine metabolites and other adenine nucleotides. Extra-cellular and intracellular ATP is broken down rapidly to ADP and AMP and finally to adenosine by 5’-nucleotidase. These metabolic events are known to occur in the myocardium, endothelium as well as in RBC. Exercise has been shown to increase metabolism of ATP in the RBC which may be an important mechanism for post exercise hypotension and cardiovascular protection. The post exercise effect was greater in hypertensive than in normotensive rats. The review summarizes current evidence in support of ATP metabolism in the RBC as potential systemic biomarker for cardiovascular protection and toxicities. It also discusses the opportunities, challenges and obstacles of exploiting ATP metabolism in RBC as target for drug development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".